Verfügbarkeit: Quantitative estimation of total body water loss during physical exercise

Quantitative estimation of total body water loss during physical exercise:

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Bibliographische Detailangaben
Beteilige Person:	Ring, Matthias (VerfasserIn)
Format:	Hochschulschrift/Dissertation Buch
Sprache:	Englisch
Veröffentlicht:	Erlangen FAU University Press 2018
Schriftenreihe:	FAU Studien aus der Informatik 4
Schlagwörter:	Biomarker Sportliche Aktivität Speichel Kern > Mathematik Maschinelles Lernen Messung Schweißabsonderung Dehydrierung machine learning total body water physical exercise dehydration kernel methods branch and bound support vector machine Gaussian kernel Hochschulschrift
Links:	http://d-nb.info/116008775X/34 https://doi.org/10.25593/978-3-96147-095-2 https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-97247 https://open.fau.de/handle/openfau/9724 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030359049&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
Umfang:	xiv, 218 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 448 g
ISBN:	9783961470945 9783961470952
DOI:	10.25593/978-3-96147-095-2

Internformat

MARC


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Datensatz im Suchindex

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adam_text	CONTENTS I INTRODUCTION 1 1 MOTIVATION, CONTRIBUTIONS, AND OVERVIEW 3 1.1 ESTIMATION OF WATER LOSS DURING PHYSICAL EXERCISE ........... 4 1.1.1 BIOIMPEDANCE AND WATER L O S S ........................ 5 1.1.2 SWEAT MARKERS AND WATER L O S S ........................ 6 1.1.3 SALIVARY MARKERS AND WATER L OSS ............................. 7 1.2 EFFICIENT AND NONLINEAR MACHINE LEARNING ......................... 8 1.2.1 KERNELS FOR BRANCH AND BOUND FEATURE SELECTION . . 9 1.2.2 AN APPROXIMATION OF THE GAUSSIAN RBF KERNEL.. . . 10 1.3 CONTRIBUTIONS TO THE PROGRESS OF RESEARCH ......................... 10 1.4 OVERVIEW OF THIS T H ESIS ........................................................ 13 II MACHINE LEARNING APPROACHES FOR WATER LOSS ESTIMATION 17 2 FUNDAMENTALS 19 2.1 GROUND TRUTH ASSESSMENT OF ABSOLUTE TOTAL BODY WATER . . 19 2.2 BIOIMPEDANCE-BASED ESTIMATION OF ABSOLUTE TOTAL BODY W ATER...................................................................................... 22 2.2.1 BASIC P RINCIPLES ........................................................ 22 2.2.2 THE MODEL IN BIOIMPEDANCE ANALYSIS .................... 23 2.2.3 THE MODEL IN BIOIMPEDANCE SPECTROSCOPY ........... 23 2.2.4 COMMENTS AND APPLICATION .................................... 24 2.3 GROUND TRUTH ASSESSMENT OF TOTAL BODY WATER L O S S ......... 26 2.4 MACHINE LEARNING FOR REGRESSION P ROBLEM S....................... 26 2.5 LEAVE-ONE-SUBJECT-OUT CROSS VALIDATION ........................... 29 3 RELATED WORK 33 3.1 CONFOUNDING FACTORS ON BIOIMPEDANCE MEASUREMENTS .. 33 3.2 SWEAT ANALYSIS FOR DEHYDRATION D IAGNOSIS ......................... 36 3.3 SALIVA ANALYSIS FOR DEHYDRATION D IAGNOSIS ......................... 37 3.4 SUMMARY OF THE STATE OF THE A RT .......................................... 40 4 DATA COLLECTION 43 4.1 SUBJECTS.................................................................................. 43 4.2 PROCEDURES PRECEDING PHYSICAL EXERCISE.............................. 45 4.2.1 PRELIMINARY EXAM INATION......................................... 45 4.2.2 STANDARDIZATION P ROCEDURES .................................... 46 4.2.3 GROUND TRUTH ASSESSMENT OF ABSOLUTE TOTAL BODY W ATER........................................................................... 47 4.2.4 ANTHROPOMETRIC M EASUREM ENTS.............................. 49 4.3 PROCEDURES DURING PHYSICAL E XERCISE .................................. 49 4.3.1 STANDARDIZATION P ROCEDURES .................................... 49 4.3.2 SUBJECTIVE ASSESSMENT OF THIRST AND F A TIG U E ......... 50 4.3.3 BIO IMPEDANCE M EASUREMENTS .................................. 51 4.3.4 TEMPERATURE M EASUREM ENTS .................................... 52 4.3.5 SWEAT COLLECTION AND ANALYSIS .................................. 53 4.3.6 SALIVARY COLLECTION AND A NALYSIS.............................. 54 4.3.7 BLOOD COLLECTION AND ANALYSIS .................................. 56 4.3.8 GROUND TRUTH ASSESSMENT OF TOTAL BODY WATER LOSS 57 4.4 THE RESULTING DATA SET AND MISSING VALUES ......................... 57 5 METHODS 61 5.1 THE COMMON AIM: ESTIMATION OF TOTAL BODY WATER LOSS . . 61 5.2 A TEMPERATURE-BASED BIOIMPEDANCE CORRECTION FOR WATER LOSS E STIM ATIO N .................................................................... 62 5.2.1 N O TA TIO N .................................................................... 62 5.2.2 PRELIM INARIES ............................................................. 62 5.2.3 A TWO-STAGE APPROACH FOR WATER LOSS ESTIMATION . 63 5.2.4 MACHINE LEARNING FOR THE BIOIMPEDANCE CORRECTION FUNCTION.................................................. 64 5.2.5 AN INTERMEDIATE STEP FOR COMMERCIAL BIOIMPEDANCE DEVICES ............................................. 65 5.2.6 CONFIGURATION OF THE MACHINE LEARNING ALGORITHMS 66 5.3 SWEAT MARKERS FOR WATER LOSS ESTIM ATION ........................... 67 5.3.1 INTERPOLATION OF MISSING VALUES................................. 67 5.3.2 STATISTICAL ANALYSIS .................................................... 68 5.3.3 MACHINE LEARNING FOR WATER LOSS ESTIM ATION ......... 66 5.4 SALIVARY MARKERS FOR WATER LOSS E STIM ATIO N ....................... 69 5.4.1 INTERPOLATION OF MISSING VALUES................................. 69 5.4.2 MINIMIZING INTER-SUBJECT VARIABILITY....................... 69 5.4.3 A PRELIMINARY ANALYSIS TO SELECT THE MACHINE LEARNING ALGORITHM .................................................. 70 5.4.4 CONFIGURATION OF THE MACHINE LEARNING ALGORITHMS 72 5.4.5 FEATURE SELECTION TO REDUCE THE SET OF SALIVARY M ARKERS...................................................................... 73 5.5 THE COMMON EVALUATION .................................................... 73 5.5.1 P RO CED U RE.................................................................. 74 5.5.2 M EASURES.................................................................... 74 6 RESULTS 77 6.1 FIRST EVALUATION MEASURE: A GRAND M E A N ........................... 77 6.2 SECOND EVALUATION MEASURE: FOCUSING ON SUBJECTS ........... 78 6.3 THIRD EVALUATION MEASURE: FOCUSING ON TIM E .................... 79 6.4 STATISTICAL ANALYSIS OF SWEAT M ARK ERS .................................. 80 6.5 FEATURE SELECTION FOR SALIVARY M ARKERS................................ 80 7 DISCUSSION 93 7.1 THE BIOIMPEDANCE AND TEMPERATURE-BASED APPROACH . . . 93 7.1.1 BIOIMPEDANCE MODELING M E TH O D ........................... 94 7.1.2 THE CHALLENGE OF OBTAINING ADEQUATE TEMPERATURE M EASUREM ENTS........................................................... 95 7.1.3 INTEGRATION INTO COMMERCIAL D EV ICES .................... 96 7.1.4 MACHINE LEARNING: LINEAR VERSUS NONLINEAR M E TH O D S.................................................................... 96 7.1.5 THE STATE-OF-THE-ART APPROACH: BIOIMPEDANCE WITHOUT CORRECTION.................................................... 97 7.2 THE SWEAT-BASED APPROACH.................................................. 97 7.2.1 SWEAT MARKERS: CORRELATION COEFFICIENTS AND PRACTICAL USEFULNESS.................................................. 96 7.2.2 DRAWBACKS OF THE MISSING VALUE INTERPOLATION .... 99 7.2.3 IMPROVEMENTS FOR FUTURE S TU D IE S ........................... 99 7.3 THE SALIVA-BASED APPROACH.................................................. 101 7.3.1 IDENTIFYING THE SALIVARY MARKERS FOR FUTURE APPLICATIONS ............................................................. 101 7.3.2 MACHINE LEARNING: LINEAR VERSUS NONLINEAR M E TH O D S.................................................................... 102 7.3.3 INTERPRETATION OF THE FEATURE SELECTION RESULTS .... 103 7.3.4 NONLINEAR NATURE OF THE SALIVARY MARKERS .............. 103 7.4 OBSERVATIONS BEYOND THE APPROACH-SPECIFIC DISCUSSIONS . 105 7.5 PUTTING THE ERROR OF THE WATER LOSS ESTIMATIONS INTO C O N TE X T ................................................................................. 107 7.6 A COMPARISON WITH THE INVASIVE WATER-DEFICIT EQUATION . . 108 7.7 RESEARCH DIRECTIONS FOR FUTURE STUDIES................................ 109 III THEORETICAL APPROACHES FOR EFFICIENT AND NONLINEAR MACHINE LEARNING 111 8 OPTIMAL FEATURE SELECTION FOR NONLINEAR DATA: BRANCH AND BOUND IN KERNEL SPACE 113 8.1 M OTIVATION............................................................................. 113 8.2 RELATED W O RK ......................................................................... 114 8.2.1 NOTATION, PRELIMINARIES, AND MONOTONIE CRITERION F U N C TIO N S .................................................................. 115 8.2.2 BRANCH AND BOUND WITHOUT HEURISTIC IM PROVEM ENTS........................................................... 116 8.2.3 BRANCH AND BOUND WITH HEURISTIC IMPROVEMENTS. . 118 8.3 BRANCH AND BOUND FEATURE SELECTION IN KERNEL SPACE .... 120 8.3.1 SEARCH M ETHOD........................................................... 120 6.3.2 TRADITIONAL CRITERION FUNCTIONS IN KERNEL SPACE . . . 120 8.3.2.1 NOTATION AND PRELIMINARIES....................... 121 6.3.2.2 THREE MAJOR C HALLENGES ........................... 122 6.3.2.3 A SOLUTION FOR THE THREE MAJOR CHALLENGES 123 5.3.2.4 MONOTONICITY IN KERNEL S P A C E .................. 124 5.3.2.5 COMMENTS AND APPLICATION....................... 126 6.3.3 MEAN CLASS DISTANCE IN KERNEL SPACE AS CRITERION F U N C TIO N .................................................................... 127 6.4 EXPERIMENTAL SETUP ............................................................. 127 6.4.1 DATA SETS AND SOFTWARE ............................................. 128 8.4.2 NUMBER OF EIGENPAIRS ............................................. 128 8.4.3 CLASSIFICATION ACCURACY AND RUNTIME .................... 129 8.5 RESULTS.................................................................................... 130 8.6 D ISCUSSION............................................................................. 131 9 AN APPROXIMATION OF THE GAUSSIAN RBF KERNEL FOR EFFICIENT SVM CLASSIFICATIONS 137 9.1 M OTIVATION............................................................................. 137 9.2 RELATED W O RK ........................................................................ 139 9.2.1 APPROXIMATIONS BASED ON THE NYSTROEM METHOD . . . 139 9.2.2 RANDOM FOURIER FEATURES OR APPROXIMATIONS BASED ON BOCHNER*S T HEOREM ............................................. 140 9.2.3 APPROXIMATIONS OF THE INTERSECTION KERNEL.............. 141 9.2.4 APPROXIMATIONS OF INNER PRODUCT K ERN ELS.............. 142 9.2.5 APPROXIMATIONS OF ADDITIVE KERNELS AND APPLICATION-ORIENTED M ETHODS................................ 142 9.2.6 APPROXIMATIONS OF THE GAUSSIAN RBF KERNEL BASED ON ALGEBRAIC REFORMULATIONS .................................... 143 9.3 M ETHODS ................................................................................. 143 9.3.1 PRELIMINARIES FOR THE KERNEL APPROXIMATION ........... 143 9.3.2 KERNEL APPROXIM ATION ............................................. 145 9.3.3 PRELIMINARIES FOR THE ERROR ANALYSIS ......................... 146 9.3.4 RELATIVE ERROR OF THE KERNEL APPROXIM ATION ........... 147 9.4 EXPERIMENTAL EVALUATION ...................................................... 150 9.4.1 VISUALIZATION OF THE RELATIVE E RRO R ........................... 150 9.4.2 VISUALIZATION OF THE DECISION BOUNDARY .................. 151 9.4.3 CLASSIFICATION EXPERIM ENTS ...................................... 152 9.4.3.1 DATA S E TS .................................................... 152 9.4.3.2 EXPERIMENTAL S E TU P .................................. 153 9.4.3.3 RESULTS ........................................................ 153 9.4.3.4 DISCUSSION.................................................. 154 9.5 COMPARISON TO RELATED WORK ............................................... 155 IV SYNTHESIS 163 10 SUMMARY, DISCUSSION OF SCIENTIFIC CONTRIBUTIONS, AND CONCLUSIONS 165 10.1 TEMPERATURE-BASED BIOIMPEDANCE CORRECTION .................. 165 10.2 SWEAT MARKERS FOR WATER LOSS ESTIM ATION ........................... 166 10.3 SALIVARY MARKERS FOR WATER LOSS E STIM ATION....................... 167 10.4 BRANCH AND BOUND FEATURE SELECTION IN KERNEL SPACE .... 169 10.5 AN APPROXIMATION OF THE GAUSSIAN RBF KERNEL .................. 170 10.6 CONCLUSIONS .......................................................................... 171 11 OUTLOOK 173 11.1 FUTURE WORK FOR WATER LOSS ESTIMATION ............................. 173 11.2 FUTURE WORK FOR EFFICIENT AND NONLINEAR MACHINE LEARNING................................................................................. 175 11.3 FUTURE WORK BEYOND INDIVIDUAL APPROACHES....................... 177 LIST OF FIGURES 181 LIST OF TABLES 183 LIST OF ABBREVIATIONS 185 BIBLIOGRAPHY 187
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genre	(DE-588)4113937-9 Hochschulschrift gnd-content
genre_facet	Hochschulschrift
id	DE-604.BV044966556
illustrated	Illustrated
indexdate	2024-12-20T18:15:31Z
institution	BVB
institution_GND	(DE-588)1068111240
isbn	9783961470945 9783961470952
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-030359049
oclc_num	1038054565
open_access_boolean	1
owner	DE-29 DE-91 DE-BY-TUM DE-29T DE-12
owner_facet	DE-29 DE-91 DE-BY-TUM DE-29T DE-12
physical	xiv, 218 Seiten Illustrationen, Diagramme 21 cm x 14.8 cm, 448 g
psigel	ebook
publishDate	2018
publishDateSearch	2018
publishDateSort	2018
publisher	FAU University Press
record_format	marc
series	FAU Studien aus der Informatik
series2	FAU Studien aus der Informatik
spellingShingle	Ring, Matthias Quantitative estimation of total body water loss during physical exercise FAU Studien aus der Informatik Biomarker (DE-588)4425928-1 gnd Sportliche Aktivität (DE-588)4182459-3 gnd Speichel (DE-588)4056095-8 gnd Kern Mathematik (DE-588)4163599-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Messung (DE-588)4038852-9 gnd Schweißabsonderung (DE-588)4180459-4 gnd Dehydrierung (DE-588)4149008-3 gnd
subject_GND	(DE-588)4425928-1 (DE-588)4182459-3 (DE-588)4056095-8 (DE-588)4163599-1 (DE-588)4193754-5 (DE-588)4038852-9 (DE-588)4180459-4 (DE-588)4149008-3 (DE-588)4113937-9
title	Quantitative estimation of total body water loss during physical exercise
title_alt	Quantitative Schätzung des Verlustes an Gesamtkörperwasser während körperlicher Betätigung
title_auth	Quantitative estimation of total body water loss during physical exercise
title_exact_search	Quantitative estimation of total body water loss during physical exercise
title_full	Quantitative estimation of total body water loss during physical exercise Matthias Ring
title_fullStr	Quantitative estimation of total body water loss during physical exercise Matthias Ring
title_full_unstemmed	Quantitative estimation of total body water loss during physical exercise Matthias Ring
title_short	Quantitative estimation of total body water loss during physical exercise
title_sort	quantitative estimation of total body water loss during physical exercise
topic	Biomarker (DE-588)4425928-1 gnd Sportliche Aktivität (DE-588)4182459-3 gnd Speichel (DE-588)4056095-8 gnd Kern Mathematik (DE-588)4163599-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Messung (DE-588)4038852-9 gnd Schweißabsonderung (DE-588)4180459-4 gnd Dehydrierung (DE-588)4149008-3 gnd
topic_facet	Biomarker Sportliche Aktivität Speichel Kern Mathematik Maschinelles Lernen Messung Schweißabsonderung Dehydrierung Hochschulschrift
url	http://d-nb.info/116008775X/34 https://doi.org/10.25593/978-3-96147-095-2 https://nbn-resolving.org/urn:nbn:de:bvb:29-opus4-97247 https://open.fau.de/handle/openfau/9724 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030359049&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
volume_link	(DE-604)BV043902000
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